Your browser doesn't support javascript.
loading
Reply to Jue et al. Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on "Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335".
Gentile, Francesco; Ferro, Matteo; Della Ventura, Bartolomeo; La Civita, Evelina; Liotti, Antonietta; Cennamo, Michele; Bruzzese, Dario; Velotta, Raffaele; Terracciano, Daniela.
Afiliação
  • Gentile F; Department of Experimental and Clinical Medicine, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy.
  • Ferro M; Division of Urology, European Institute of Oncology (IEO), IRCCS, Via Ripamonti 435, 20141 Milan, Italy.
  • Della Ventura B; Department of Physics "Ettore Pancini", University of Naples "Federico II", Via Cintia 26 Ed. G, 80126 Naples, Italy.
  • La Civita E; Department of Translational Medical Sciences, University of Naples "Federico II", 80131 Naples, Italy.
  • Liotti A; Department of Translational Medical Sciences, University of Naples "Federico II", 80131 Naples, Italy.
  • Cennamo M; Department of Translational Medical Sciences, University of Naples "Federico II", 80131 Naples, Italy.
  • Bruzzese D; Department of Public Health, University of Naples "Federico II", 80131 Naples, Italy.
  • Velotta R; Department of Physics "Ettore Pancini", University of Naples "Federico II", Via Cintia 26 Ed. G, 80126 Naples, Italy.
  • Terracciano D; Department of Translational Medical Sciences, University of Naples "Federico II", 80131 Naples, Italy.
Diagnostics (Basel) ; 11(7)2021 Jul 06.
Article em En | MEDLINE | ID: mdl-34359297
ABSTRACT
In their comment "Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer [...].

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article